The present invention relates generally to the field of communication, and more particularly to spam messages.
In the field of spam protection, identifying new and previously unknown spam messages is important to the quality and accuracy of any anti-spam solution, in particular for the development and improvement of anti-spam software, as said messages provide the option to derive new signatures and patterns for the protection of customers.
While heuristics and statistical learning methods for identifying spam messages have made great strides in the last few years, still more than half of the spam messages coverage by anti-spam software is provided based on traditional signature or pattern based methods. Furthermore, additionally feeding heuristics and learning methods with current data based on harvested spam messages may further increase the effectiveness of these methods.